Tracking Dependent Extended Targets Using Multi-Output Spatiotemporal Gaussian Processes
Bibliographic record
Abstract
In Extended Target Tracking, where estimating the shape is essential as kinematic, exploiting the dependencies between targets is often an excellent way to enhance performance. In a group of dependent targets, sampled features tend to have spatially and temporally correlations inside and between frames. Gaussian process regression has been used as a powerful Bayesian semi-supervised method to describe functions’ spatial and temporal correlation. This paper exploits and models the dependency between extended targets using Gaussian Process. We propose a novel recursive approach called Multi-Output Spatio-Temporal Gaussian Process Kalman Filter (MO-STGP-KF) to estimate and track multiple dependent extended targets that have possibly been degraded or covered with clutter. We used this method for detecting and tracking the group of connected lane markings called “lane-lines”. For detection and clustering, we propose a new Kernel-based Joint Probabilistic Data Association Coupled Filter (K-JPDACF) to cluster point features belonging to each lane-line. Compared to recently published model-based multi-lane tracking, semi-supervised, and fully supervised lane detection methods, our method shows 13 percent 34 percent and 20 percent improvement in accuracy, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".